1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
24/12/2023 Comida 19950 Andrés Empanadas
25/12/2023 Uber 5595 Tami NA
27/12/2023 Diosi 22970 Andrés arena
30/12/2023 Comida 50000 Andrés jumbo la litad de los 97
2/1/2024 Electricidad 55466 Andrés pac enel
2/1/2024 Comida 44542 Tami Supermercado
5/1/2024 Comida 9516 Tami Burger King pre Maite
5/1/2024 Comida 19590 Tami Barritas Soul
8/1/2024 Comida 57625 Tami Supermercado
13/1/2024 Limpieza alfombras 60000 Tami NA
13/1/2024 Comida 33110 Andrés NA
15/1/2024 Comida 98241 Tami Supermercado
17/1/2024 VTR 21990 Andrés NA
22/1/2024 Netflix 8304 Tami NA
22/1/2024 Comida 44016 Tami Supermercado
23/1/2024 Comida 7500 Andrés NA
27/1/2024 Jardinero 40000 Tami NA
29/1/2024 Comida 65786 Tami Supermercado
30/1/2024 Electricidad 55759 Andrés NA
3/2/2024 donación 50000 Andrés NA
4/2/2024 Comida 46309 Andrés NA
5/2/2024 Comida 33079 Tami Supermercado
8/2/2024 Enceres 35440 Andrés casaideas
18/2/2024 VTR 21990 Andrés NA
22/2/2024 Netflix 8393 Tami NA
25/2/2024 Enceres 4973 Andrés colgador manguera
25/2/2024 Enceres 7980 Andrés adaptador vorriente y adaptadores manguera
27/2/2024 Enceres 49980 Andrés detergente
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.2345e+08   2    7.7763  5e-04 ***
## lag_depvar    9.8396e+10   1 1858.4144 <2e-16 ***
## Residuals     3.5527e+10 671                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   927.5339 13530.14 0.0197407
## 2-0 29517.114 23802.0434 35232.18 0.0000000
## 2-1 22288.276 18929.4602 25647.09 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
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## 21   20390.57             0   20986.00
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## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
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## 239  45392.57             2   42928.29
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## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
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## 248  37430.14             2   34197.57
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## 250  33729.86             2   26932.43
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## 252  44028.00             2   38081.43
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## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
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## 267  51426.86             2   41348.57
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## 269  51907.43             2   47160.57
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## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
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## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
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## 283  43064.14             2   44899.43
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## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
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## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
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## 302  82013.86             2   87702.86
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## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
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## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
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## 318  62730.57             2   65201.86
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## 320  73744.86             2   64589.14
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## 325  74746.14             2   76122.29
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## 333  32582.71             2   33337.29
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## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
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## 344  39832.14             2   37787.71
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## 346  41633.57             2   41917.86
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## 363  50223.00             2   53103.86
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## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
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## 421  46352.43             2   42246.71
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## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
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## 431  39328.29             2   39456.29
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## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
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## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
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## 445  38114.00             2   43555.29
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## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
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## 454  47139.43             2   42882.86
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## 456  41099.00             2   35547.57
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## 459  48554.29             2   44524.57
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## 463  50720.71             2   50490.00
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## 467  52457.00             2   55515.57
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## 472  42305.57             2   46170.00
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## 474  55149.57             2   46605.57
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## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
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## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
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## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   519 51751.38 15377.088
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   1933.678302   4003.859554   -501.793209   2469.521920  -2897.892799 
##             7             8             9            10            11 
##    544.821164  -5617.510180  -1230.983467  -4013.808647   -511.033854 
##            12            13            14            15            16 
##  -5019.491045  -1745.045772  -1034.498011    254.113707  -3337.214624 
##            17            18            19            20            21 
##   -500.920451  -2235.417171   6488.137419  -1524.356792  -1219.124725 
##            22            23            24            25            26 
##   1455.884516  -1174.066391    235.667684   1707.181865  -7058.311034 
##            27            28            29            30            31 
##    887.694695   8162.142229    521.972008     90.853353  -2301.249008 
##            32            33            34            35            36 
##   1635.099113   4655.416945   1275.548697   2546.096936  -1689.211346 
##            37            38            39            40            41 
##   4744.238419   4384.153318  -2140.244916  -2896.329166  -1079.582215 
##            42            43            44            45            46 
## -10730.639479   7139.176636   2535.417202   1386.566129   8143.446970 
##            47            48            49            50            51 
##    841.210733   6675.169260   6940.431510  -5583.939885  -4621.769164 
##            52            53            54            55            56 
##  -4978.835816  -7932.694469   6008.577724  -4090.448885  -4966.595431 
##            57            58            59            60            61 
##   3720.269084    827.543521    -70.926210    108.462536  -5023.180920 
##            62            63            64            65            66 
##  18029.500471   3826.743003  -3428.500889   6061.834051   7552.441378 
##            67            68            69            70            71 
##  14931.345273   2168.002192 -12771.726436  -1117.030076   4790.082510 
##            72            73            74            75            76 
##  -4702.169214  -4303.704090 -10474.201853   2331.624537  -5480.270289 
##            77            78            79            80            81 
##    913.828966  -6980.607053    345.571513  -2521.555875  -2874.025270 
##            82            83            84            85            86 
##  -4128.595740   -767.131765   2106.769170   3616.241675    405.598891 
##            87            88            89            90            91 
##   -539.132158    142.326680   4258.057429  -1136.697376   1157.178761 
##            92            93            94            95            96 
##  -2041.163644  -1053.996545    154.221674    257.668109  -7494.250274 
##            97            98            99           100           101 
##   2272.232761  -8670.625300  -3127.179846  -4245.315543  -1975.691484 
##           102           103           104           105           106 
##  -1495.002266   2958.950603  -2487.968452   2432.468724  -1260.287785 
##           107           108           109           110           111 
##    864.940377   2510.021512  -3182.592798  -4793.872452   -981.593885 
##           112           113           114           115           116 
##   1776.880109  11611.903427  -1139.455891   2740.875502   4366.440883 
##           117           118           119           120           121 
##   3657.311114   -911.999888  -4567.691616  -3663.543396   2318.108523 
##           122           123           124           125           126 
##  -1698.851431   1344.974164   8882.909850   1001.085774    278.352203 
##           127           128           129           130           131 
##  -2389.328684   2733.636416   7161.399260   1213.167908  -8308.210824 
##           132           133           134           135           136 
##   1790.651271   4198.456467  -3046.761156  -1363.639532   -825.340933 
##           137           138           139           140           141 
##  -3866.947655   1137.525769   -516.936157  -2938.992662   1653.401609 
##           142           143           144           145           146 
##  -1911.417740  -7883.056867   1876.844704  -3590.800306   1954.121359 
##           147           148           149           150           151 
##   -355.005565    934.617828   -420.716432   1293.827221   1156.361722 
##           152           153           154           155           156 
##   3348.399074  -4818.412541  -1208.151368  -3282.022289   5868.927368 
##           157           158           159           160           161 
##   9759.103434  -3561.806070  -4936.430916   3401.359386     77.578670 
##           162           163           164           165           166 
##   2602.883917  -5946.105204  -6859.957748   3964.700068  17291.881858 
##           167           168           169           170           171 
##   3794.985027   -195.526716  -2269.895580   -981.179192   3686.482507 
##           172           173           174           175           176 
##    -87.977351  -7951.846496   2859.097953   4367.164737    728.646094 
##           177           178           179           180           181 
##   8853.632838  -9028.099514  -3408.736922 -10735.662612 -11383.399925 
##           182           183           184           185           186 
##    951.563107   9067.771688  -1489.204900   5859.955436   6583.316915 
##           187           188           189           190           191 
##  13278.126422   8723.159561  -3689.079892   2732.059683  10635.437107 
##           192           193           194           195           196 
##  -1265.746220  -2139.746322 -10050.409858  -6308.253317   1192.053043 
##           197           198           199           200           201 
##  -5251.245499  -9884.301867   5170.304493  -3175.978827  -1847.494086 
##           202           203           204           205           206 
##   -943.953106   6363.001242   9858.488576    694.153796   3032.773209 
##           207           208           209           210           211 
##   3232.284793   5944.081261  13052.972915  -5317.998938 -11053.705419 
##           212           213           214           215           216 
##  -5612.010807 -10617.834141  -5247.549562   1308.058366 -13177.594428 
##           217           218           219           220           221 
##  16065.066418   7756.135040   1590.395439  26763.743414  12959.341303 
##           222           223           224           225           226 
##   7883.018678  14603.345031  -3219.563621  -1192.185556   4230.288080 
##           227           228           229           230           231 
##    806.495397   3140.552993   9387.584753   6291.631938  -1419.645110 
##           232           233           234           235           236 
##  -1428.172932   9750.556341 -11092.956356  -7080.416431  -8464.599057 
##           237           238           239           240           241 
## -10152.502943   2893.984854   1236.582442  -8377.235622  -9172.777619 
##           242           243           244           245           246 
##   8812.352216  -7883.584502   2278.439010 -10447.048376  -4321.943843 
##           247           248           249           250           251 
##   1134.801111    773.319392 -12500.981941   3313.360304   1826.343925 
##           252           253           254           255           256 
##   4035.173704   2039.135981  -1214.499095  11076.137010  20977.182689 
##           257           258           259           260           261 
##   3561.377736  -3914.200827   4359.361666  -1423.789968   3947.538619 
##           262           263           264           265           266 
##  -4620.090582 -10759.917441  -4761.783520   -615.975214  -5278.589847 
##           267           268           269           270           271 
##   8627.748993  -4295.186140   4116.157285  -2117.111339   4390.764732 
##           272           273           274           275           276 
##    730.394145   7327.621680  -1297.149879  12101.841287  -4363.514546 
##           277           278           279           280           281 
##   1850.355517   -246.015503   7951.259646  -4872.273419  -2641.207554 
##           282           283           284           285           286 
## -11222.340093  -2784.940883  18517.603503   7904.445840   2935.697608 
##           287           288           289           290           291 
##   -423.043009   1071.394013   6548.741595   7090.936213 -18507.519189 
##           292           293           294           295           296 
## -11152.797977  -8276.346428   9427.448207   2989.941562  -1211.872955 
##           297           298           299           300           301 
##  27357.481146  10372.277085   5290.735349   9914.259271   3316.909710 
##           302           303           304           305           306 
##   -600.831936   8254.252927 -23888.514449  -3487.713423   -178.018811 
##           307           308           309           310           311 
##  -6971.727134  -4060.314646   2808.367094  -9260.354723  -3400.129180 
##           312           313           314           315           316 
##  -8369.193124   1310.489044  -3347.830179   1843.119928  -4231.479536 
##           317           318           319           320           321 
##  27268.770558   -557.093780   3424.165440  10983.477149   5852.118988 
##           322           323           324           325           326 
##  32674.471147   5762.274546 -20310.549936   2078.477485   1380.073973 
##           327           328           329           330           331 
##  -6217.892320  -1598.634995 -33170.181895    599.644291  -2523.046804 
##           332           333           334           335           336 
##   -299.502378  -3335.174797   3914.671572   -524.523563  -7022.410645 
##           337           338           339           340           341 
##  -3248.813738  -2331.380670  -7814.498495   3655.482118  -1483.680259 
##           342           343           344           345           346 
##  -1840.127197  -1091.679499     91.599704    421.271156  -1654.519371 
##           347           348           349           350           351 
##  -9486.906239 -13347.039189   2045.596987  -4513.504547  -3862.854341 
##           352           353           354           355           356 
##  -6188.337187   1511.486623   1208.592009   2626.824735  -3835.259501 
##           357           358           359           360           361 
##   -611.838514    596.955520   6959.983061    318.722530      8.201643 
##           362           363           364           365           366 
##   2629.623341  -2673.201061   -834.572858  -8707.837874  -4685.048310 
##           367           368           369           370           371 
##  -6305.136292  -5088.900974  -7417.594715   4801.538936    253.519686 
##           372           373           374           375           376 
##   7031.813029  -7622.844340  -2328.151964  -3458.993802  -2555.616156 
##           377           378           379           380           381 
## -12550.491321   1692.834073 -10782.527026   5458.752575   9205.714309 
##           382           383           384           385           386 
##   3126.999334  -2355.399765   1622.357809   6784.544525  11526.967606 
##           387           388           389           390           391 
##  -5571.329514  -5228.704928    -99.973252   8614.646319   1957.581157 
##           392           393           394           395           396 
##  11366.524422  -9628.663177   2877.268241    833.002674    674.683508 
##           397           398           399           400           401 
##   -549.948123   -479.854473 -14420.407738   8428.231680  -1158.213699 
##           402           403           404           405           406 
##  -1358.768740   6985.726183  -7845.733857  -1301.406135  -2539.641498 
##           407           408           409           410           411 
##  -5842.850732  -2930.892934  -3997.568075  -8854.644590   5965.101959 
##           412           413           414           415           416 
##   1573.837876  -7402.613347  -7786.791383  14067.623548   3845.832735 
##           417           418           419           420           421 
##   4563.409614  -7921.371053  -4725.320887  -2625.753920   2781.869000 
##           422           423           424           425           426 
## -14002.267382  -2924.800182  -9230.957148   2813.511868   6850.313570 
##           427           428           429           430           431 
##   6551.786813  -3928.112165  -4103.291879  -4740.932424  -1845.461289 
##           432           433           434           435           436 
##  -5767.659741  -6725.002565  -6094.299875  -1570.239903  -1002.871414 
##           437           438           439           440           441 
##  -5105.657219   2427.122954   4746.209929  -5073.561687  -2216.714435 
##           442           443           444           445           446 
##   1514.094737  -3862.133615   2783.645648  -6580.544783 -12172.946078 
##           447           448           449           450           451 
##  -4685.795663   9458.617368  -2073.287038   4709.286758  -5843.282798 
##           452           453           454           455           456 
##  -1151.974940    359.346240   3022.460032 -12225.539398   3282.606639 
##           457           458           459           460           461 
##  -6725.169673   6440.188581   3027.182017   2565.896555  -3754.925436 
##           462           463           464           465           466 
##   2140.662694     69.661413   1871.491531   -421.532653   3442.480935 
##           467           468           469           470           471 
##  -2510.720530   5898.982271  -6783.903469  -2891.317309  -2160.686023 
##           472           473           474           475           476 
##  -4634.857778   2984.457064   7835.012210  -5883.776819   1546.121757 
##           477           478           479           480           481 
##  -6094.404116  -2825.923815   2011.167502 -12893.071204  -9854.651706 
##           482           483           484           485           486 
##  -1383.645487   -134.635650  -1080.593898  -1439.986973  -9669.729935 
##           487           488           489           490           491 
##  10927.929524   6231.365252   7501.596524  -5268.991650   5464.137682 
##           492           493           494           495           496 
##   9450.741696   6307.744952 -13173.772861 -10445.874444  -3441.972329 
##           497           498           499           500           501 
##  -1129.907489   -542.001249  -7631.527507    536.023558   4246.269942 
##           502           503           504           505           506 
##   5537.522063    763.155147    191.898640  -7126.818707    600.143477 
##           507           508           509           510           511 
##  -4999.900072   1832.880538  -1259.300718  -8124.714713   -650.515731 
##           512           513           514           515           516 
##  -2707.006899   -630.207215   1304.905031  -9487.630494  -7851.404850 
##           517           518           519           520           521 
##  24139.147487   9990.510406   6147.898459  -5023.640119   3020.190565 
##           522           523           524           525           526 
##  17255.780696  11883.779110 -23653.171549  -4894.289838  -3634.262447 
##           527           528           529           530           531 
##   4629.214435   -241.797968 -10994.088073   4373.013120  13958.106774 
##           532           533           534           535           536 
##  -4763.439657   4513.775731   5738.007524  -1557.322168  -4347.847949 
##           537           538           539           540           541 
##  -6945.865640  -2056.022588   8351.927396    261.632829  -8010.098410 
##           542           543           544           545           546 
##   1851.193356   -533.730027    430.994056 -10956.107386 -11107.556003 
##           547           548           549           550           551 
##   1890.777422   6913.188492  -1300.271313    852.228181  -7682.588800 
##           552           553           554           555           556 
##   8525.392590    985.536958 -11851.044851   9121.337450   8740.884968 
##           557           558           559           560           561 
##    282.437350   5025.458641  -3357.721263  14267.479932  21808.153359 
##           562           563           564           565           566 
##  -5945.234035  -9293.922122   7010.451750    517.928076   3720.624958 
##           567           568           569           570           571 
##  -7096.613152 -17136.324774   6630.361656   6498.835301   2046.396416 
##           572           573           574           575           576 
##   3259.390757   1959.262026  -1966.539022  14878.579072  -9328.400913 
##           577           578           579           580           581 
##  -6065.102027   8809.000169   3056.212801  -6328.351896   7635.901864 
##           582           583           584           585           586 
##  -3590.178759  -2623.543541  15817.069564 -14207.062670   8528.232404 
##           587           588           589           590           591 
##    267.750129  -6026.381951   -648.972091    350.253854 -10549.314252 
##           592           593           594           595           596 
##   1781.344111  -7120.167053   3023.973657   8881.011058  -7369.901830 
##           597           598           599           600           601 
##   5894.516758   2855.479022   7007.156821  -2965.122673   6323.069794 
##           602           603           604           605           606 
##  -8062.041229   2380.474207   1420.625479   3299.558355   1687.134568 
##           607           608           609           610           611 
##    596.973560  -5616.897634   8192.167149   -972.100755  -2387.638953 
##           612           613           614           615           616 
##  -3304.093414  -8120.987869  11974.821276   5107.193833  -9091.929111 
##           617           618           619           620           621 
##  11728.767029   6291.461305  -5276.049431  26570.267217 -12316.192253 
##           622           623           624           625           626 
##  -6468.819224   3366.395886  -3922.318064 -10416.938692  11343.298406 
##           627           628           629           630           631 
## -21443.125575  -2484.701581   8606.014640  11197.792140  -1347.924393 
##           632           633           634           635           636 
##  33462.181500  -6020.453933   6148.416607   5858.785378  -1788.114837 
##           637           638           639           640           641 
##  -4937.261479  -1631.216309 -12167.554924  -2142.765051  -1807.243741 
##           642           643           644           645           646 
##  -2454.799316  -2811.594123   1840.995584   4494.670265  17092.269615 
##           647           648           649           650           651 
##  18883.989935   1408.161699   5271.224732  11103.874977  20721.260795 
##           652           653           654           655           656 
##   1501.673160 -27377.540002  -1062.335106  -2043.233762   2078.544366 
##           657           658           659           660           661 
##  -2963.747325 -10439.157499   1717.022149   4315.319247   -854.893327 
##           662           663           664           665           666 
##  13174.524232   1666.321844   2118.002109 -11382.103940   1525.450761 
##           667           668           669           670           671 
##   1354.550819  -4982.708431  -7291.095185   2102.498608  -3630.298249 
##           672           673           674           675           676 
##   2722.112795  -3279.144654  -9268.213032  -8340.901389  -3091.213669 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17335.61 20135.14 24317.94 24040.62 26354.61 23731.89 24436.22 19748.13 
##       10       11       12       13       14       15       16       17 
## 19489.09 16876.32 17640.78 14424.90 14475.21 15128.74 16796.93 15145.06 
##       18       19       20       21       22       23       24       25 
## 16162.42 15546.43 22510.36 21609.70 21098.26 22956.64 22293.90 22935.53 
##       26       27       28       29       30       31       32       33 
## 24750.60 18780.59 20477.86 28184.03 28240.72 27919.11 25588.19 26967.15 
##       34       35       36       37       38       39       40       41 
## 30745.88 31088.47 32474.07 30026.33 34058.85 37213.24 34318.61 31182.87 
##       42       43       44       45       46       47       48       49 
## 30049.93 20787.11 28180.01 30575.72 31646.70 38370.36 37873.40 42457.57 
##       50       51       52       53       54       55       56       57 
## 46622.94 39443.05 34102.41 29208.41 22467.57 28652.31 25290.17 21649.73 
##       58       59       60       61       62       63       64       65 
## 25984.31 27222.78 27514.82 27919.75 23859.79 40173.40 41986.50 37312.02 
##       66       67       68       69       70       71       72       73 
## 41448.56 46281.94 56771.57 54818.58 40308.74 37856.35 40823.74 35219.28 
##       74       75       76       77       78       79       80       81 
## 30747.63 21606.66 24754.56 20748.46 22799.61 17780.57 19762.27 19001.74 
##       82       83       84       85       86       87       88       89 
## 18045.74 16146.99 17403.37 20951.04 25294.83 26268.13 26292.67 26899.09 
##       90       91       92       93       94       95       96       97 
## 30955.13 29805.25 30787.88 28884.71 28097.92 28459.90 28859.68 22544.62 
##       98       99      100      101      102      103      104      105 
## 25509.20 18656.32 17531.60 15605.12 15899.86 16565.91 20963.68 20062.53 
##      106      107      108      109      110      111      112      113 
## 23514.86 23308.35 24956.41 27785.02 25325.02 21828.02 22098.83 24700.81 
##      114      115      116      117      118      119      120      121 
## 35383.46 33606.55 35413.27 38361.40 40284.57 38011.69 32919.40 29322.03 
##      122      123      124      125      126      127      128      129 
## 31369.99 29678.74 30840.52 38313.06 37961.50 37038.76 33954.79 35706.17 
##      130      131      132      133      134      135      136      137 
## 41013.69 40463.35 31812.35 33055.97 36192.33 32663.07 31077.34 30177.66 
##      138      139      140      141      142      143      144      145 
## 26792.33 28183.08 27956.56 25681.60 27672.13 26319.91 20029.16 23008.94 
##      146      147      148      149      150      151      152      153 
## 20872.02 23799.29 24330.24 25894.00 26073.03 27699.50 28978.46 31959.84 
##      154      155      156      157      158      159      160      161 
## 27505.87 26781.17 24377.36 30172.75 41582.23 39940.43 37349.50 42285.71 
##      162      163      164      165      166      167      168      169 
## 43670.69 47029.39 42571.24 37957.01 43291.40 59320.59 61495.67 59936.32 
##      170      171      172      173      174      175      176      177 
## 56815.18 55241.23 57898.55 56938.99 49360.19 52136.41 55816.35 55851.94 
##      178      179      180      181      182      183      184      185 
## 62861.39 53522.74 50328.09 41290.69 32971.72 36421.23 46355.49 45820.62 
##      186      187      188      189      190      191      192      193 
## 51673.68 57322.45 67924.84 73119.22 66919.51 67109.71 74061.60 69810.46 
##      194      195      196      197      198      199      200      201 
## 65408.27 54832.25 48962.38 50362.82 46031.30 38331.27 44648.41 42905.49 
##      202      203      204      205      206      207      208      209 
## 42549.52 43019.86 49700.08 58440.42 58076.23 59772.14 61400.20 65127.88 
##      210      211      212      213      214      215      216      217 
## 74435.86 66651.28 55038.15 49737.26 40884.41 37893.08 40954.59 31141.93 
##      218      219      220      221      222      223      224      225 
## 47831.15 55029.32 55916.11 78300.23 85669.70 87639.37 95103.56 86206.04 
##      226      227      228      229      230      231      232      233 
## 80305.00 79893.93 76600.02 75775.56 80433.23 81774.65 76303.32 71596.44 
##      234      235      236      237      238      239      240      241 
## 77155.38 64026.85 56196.74 48282.22 40034.30 44155.99 46272.66 39833.06 
##      242      243      244      245      246      247      248      249 
## 33618.50 43728.73 38071.99 41941.76 34335.23 33062.77 36656.82 39433.41 
##      250      251      252      253      254      255      256      257 
## 30416.50 36255.08 39992.83 45100.58 47773.36 47274.43 57402.82 74606.91 
##      258      259      260      261      262      263      264      265 
## 74425.06 67847.78 69304.79 65588.89 67010.80 60873.06 50327.35 46421.26 
##      266      267      268      269      270      271      272      273 
## 46627.16 42799.11 51455.76 47791.27 51868.54 50016.66 54015.89 54306.95 
##      274      275      276      277      278      279      280      281 
## 60223.58 57897.44 67408.37 61434.93 61641.44 60018.17 65664.84 59500.35 
##      282      283      284      285      286      287      288      289 
## 56121.77 45849.08 44272.68 61216.27 66653.73 67056.33 64517.18 63619.83 
##      290      291      292      293      294      295      296      297 
## 67553.78 71398.52 52713.37 42981.20 37092.55 47241.06 50428.59 49557.38 
##      298      299      300      301      302      303      304      305 
## 73348.44 79194.26 79850.74 84385.95 82614.69 77728.18 81136.94 56456.14 
##      306      307      308      309      310      311      312      313 
## 52779.88 52465.01 46359.17 43615.35 47158.35 39835.27 38578.76 33231.37 
##      314      315      316      317      318      319      320      321 
## 36952.54 36147.59 39914.91 37933.09 63287.67 61164.98 62761.38 70625.60 
##      322      323      324      325      326      327      328      329 
## 72972.96 98028.01 96432.84 72667.67 71485.64 69870.46 61956.92 59127.32 
##      330      331      332      333      334      335      336      337 
## 29578.78 33204.62 33636.79 35917.89 35269.76 40940.24 41997.84 37324.96 
##      338      339      340      341      342      343      344      345 
## 36552.52 36677.07 32074.38 37972.97 38625.27 38879.39 39740.54 41496.59 
##      346      347      348      349      350      351      352      353 
## 43288.09 43043.91 36106.61 26832.26 32087.50 30967.57 30564.48 28220.80 
##      354      355      356      357      358      359      360      361 
## 32821.41 36512.89 40901.83 39121.12 40360.33 42463.02 49734.56 50275.94 
##      362      363      364      365      366      367      368      369 
## 50474.23 52896.20 50421.72 49875.55 42643.76 39887.42 36128.33 33944.17 
##      370      371      372      373      374      375      376      377 
## 30067.89 37233.91 39482.62 47236.27 41308.72 40765.14 39326.90 38867.49 
##      378      379      380      381      382      383      384      385 
## 29887.88 34409.10 27576.96 35658.86 45819.14 49324.97 47627.21 49585.60 
##      386      387      388      389      390      391      392      393 
## 55701.75 65028.62 58353.42 52914.12 52647.35 59903.56 60418.19 68941.95 
##      394      395      396      397      398      399      400      401 
## 58229.73 59770.43 59337.89 58830.38 57342.57 56124.84 43104.77 51546.93 
##      402      403      404      405      406      407      408      409 
## 50564.05 49547.56 55841.88 48508.98 47831.64 46186.28 41935.75 40786.00 
##      410      411      412      413      414      415      416      417 
## 38882.22 33075.04 40816.30 43693.76 38455.08 33625.38 48248.60 52029.16 
##      418      419      420      421      422      423      424      425 
## 55892.80 48487.75 44872.47 43570.56 47097.12 35709.66 35443.39 29798.06 
##      426      427      428      429      430      431      432      433 
## 35294.54 43483.07 50260.11 47079.58 44197.22 41173.75 41063.80 37600.43 
##      434      435      436      437      438      439      440      441 
## 33803.30 31083.53 32633.30 34451.80 32489.73 37274.65 43376.56 40183.14 
##      442      443      444      445      446      447      448      449 
## 39894.05 42850.28 40771.64 44694.54 40020.80 31202.80 30059.67 41227.00 
##      450      451      452      453      454      455      456      457 
## 40913.86 46470.71 42179.69 42523.51 44116.97 47773.11 37816.39 42584.74 
##      458      459      460      461      462      463      464      465 
## 38084.38 45527.10 48988.39 51565.21 48349.34 50651.05 50849.22 52567.10 
##      466      467      468      469      470      471      472      473 
## 52073.09 54967.72 52340.59 57307.47 50679.89 48330.69 46940.43 43621.11 
##      474      475      476      477      478      479      480      481 
## 47314.56 54653.35 49173.31 50848.12 45723.92 44129.98 46915.64 36506.51 
##      482      483      484      485      486      487      488      489 
## 30175.50 32013.64 34665.31 36130.42 37080.16 30827.07 43148.21 49697.26 
##      490      491      492      493      494      495      496      497 
## 56413.56 51213.29 55965.69 63471.97 67219.77 53705.45 44440.54 42498.48 
##      498      499      500      501      502      503      504      505 
## 42816.29 43594.24 38172.98 40531.87 45744.91 51331.70 52029.53 52138.25 
##      506      507      508      509      510      511      512      513 
## 45945.29 47262.90 43584.55 46294.02 45965.29 39785.94 40898.15 40087.06 
##      514      515      516      517      518      519      520      521 
## 41174.24 43770.20 36729.83 32088.00 55578.92 63603.39 67195.35 60684.95 
##      522      523      524      525      526      527      528      529 
## 62002.08 75360.94 82221.17 57589.58 52545.26 49294.79 53600.66 53115.23 
##      530      531      532      533      534      535      536      537 
## 43462.70 48371.18 60820.30 55432.65 58773.56 62694.75 59796.56 54910.29 
##      538      539      540      541      542      543      544      545 
## 48481.74 47160.07 54964.65 54719.24 47403.52 49590.02 49419.58 50101.82 
##      546      547      548      549      550      551      552      553 
## 40906.98 32879.08 37148.38 45129.41 44929.77 46607.16 40717.04 49579.46 
##      554      555      556      557      558      559      560      561 
## 50715.47 40665.38 50046.97 57778.42 57153.97 60691.58 56529.52 68093.56 
##      562      563      564      565      566      567      568      569 
## 84503.38 74759.92 63514.55 67859.93 66015.66 67182.47 58893.32 43149.92 
##      570      571      572      573      574      575      576      577 
## 50041.45 55847.89 57010.89 59051.74 59687.97 56862.42 68904.40 58455.39 
##      578      579      580      581      582      583      584      585 
## 52283.29 59757.79 61236.64 54446.10 60607.89 56257.97 53351.93 66695.21 
##      586      587      588      589      590      591      592      593 
## 52367.34 59588.82 58696.38 52523.54 51840.32 52111.74 42982.80 45732.88 
##      594      595      596      597      598      599      600      601 
## 40449.17 44623.99 53240.76 46683.48 52444.52 54782.56 60356.84 56579.22 
##      602      603      604      605      606      607      608      609 
## 61312.47 53022.10 54870.66 55634.01 57903.58 58468.03 58016.47 52291.26 
##      610      611      612      613      614      615      616      617 
## 59234.82 57327.35 54473.09 51234.27 44314.89 55632.66 59455.07 50542.09 
##      618      619      620      621      622      623      624      625 
## 60770.11 64885.05 58483.73 80339.48 65711.10 58168.75 60138.18 55569.22 
##      626      627      628      629      630      631      632      633 
## 46066.27 56594.55 37476.13 37338.70 46746.92 57054.21 55131.53 83379.88 
##      634      635      636      637      638      639      640      641 
## 73730.30 75894.21 77504.11 72318.69 65159.79 61850.41 49957.77 48353.39 
##      642      643      644      645      646      647      648      649 
## 47263.51 45771.17 44182.86 46814.90 51355.02 66075.30 80258.12 77429.63 
##      650      651      652      653      654      655      656      657 
## 78318.27 84091.45 97311.04 92157.40 62925.19 60419.66 57425.03 58393.18 
##      658      659      660      661      662      663      664      665 
## 54893.73 45466.98 47811.40 52056.89 51262.62 62630.82 62510.57 62795.25 
##      666      667      668      669      670      671      672      673 
## 51443.98 52780.73 53782.14 49198.95 43279.50 46263.58 43902.60 47331.00 
##      674      675      676 
## 45121.07 38078.62 32826.07 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8252
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original    bias    std. error
## t1*    7.776264  0.526092    3.541566
## t2* 1858.414361 23.646462  224.170125
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter  Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.2329       7.850313   14.80168
## 2    lag_depvar 1540.2189    1868.191709 2276.55401

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Tue Feb 27 22:48:30 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Tue Feb 27 22:48:36 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Tue Feb 27 22:48:43 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Tue Feb 27 22:48:50 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Tue Feb 27 22:48:57 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Tue Feb 27 22:49:03 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Tue Feb 27 22:49:10 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Tue Feb 27 22:49:17 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Tue Feb 27 22:49:24 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Tue Feb 27 22:49:30 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 0.000 5.195333 5.410333 5.849167 6.33422
Comida 379.926 366.009167 310.278417 317.896583 342.09456
Comunicaciones 0.000 0.000000 0.000000 0.000000 0.00000
Electricidad 111.225 38.104750 47.072333 29.523000 34.69762
Enceres 0.000 18.259750 20.086417 14.801167 24.08570
Farmacia 0.000 4.733250 1.831667 13.996083 8.30346
Gas/Bencina 0.000 35.219333 44.325000 13.583667 26.49272
Diosi 0.000 55.804250 31.180667 52.687833 42.43058
donaciones/regalos 0.000 0.000000 0.000000 14.340167 5.49438
Electrodomésticos/ Mantención casa 60.000 0.000000 3.944000 56.595000 17.78936
VTR 21.990 12.829167 25.156667 19.086917 19.16436
Netflix 8.304 4.555500 7.151583 7.028750 6.80988
Otros 0.000 0.000000 3.151083 0.000000 0.75626
Total 581.445 540.710500 499.588167 545.388333 534.45310
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2260, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-03-09 00:04:58 sería de: 37.693 pesos// Percentil 95% más alto proyectado: 40.841,28

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36984.46 36981.15
Lo.80 37035.93 37046.95
Point.Forecast 37693.41 39585.60
Hi.80 39470.68 44491.94
Hi.95 40444.78 47089.19


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2560  1018.5601
## s.e.  0.1297    28.7097
## 
## sigma^2 = 28486:  log likelihood = -391.87
## AIC=789.74   AICc=790.17   BIC=796.02
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1  intercept     xreg
##       0.2148   646.9888  11.8850
## s.e.  0.1307   252.1452   7.9946
## 
## sigma^2 = 28085:  log likelihood = -390.91
## AIC=789.82   AICc=790.55   BIC=798.2
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 791.8612 676.3560 750.3598
Lo.80 908.1475 794.8049 834.0495
Point.Forecast 1127.8174 1018.5601 1021.9135
Hi.80 1347.4873 1242.3152 1264.5914
Hi.95 1463.7736 1360.7641 1415.5848


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))